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Theory and inference for a Markov switching GARCH model

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Bauwens, Luc [UCL] Preminger, Arie [UCL] Rombouts, Jeroen [UCL]

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on S&P500 daily returns.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès interdit
Publication date 2009
Language Anglais
Journal information "The Econometrics Journal" - Vol. 13, no. 2, p. 218-244 (2010)
Peer reviewed yes
Publisher Wiley-Blackwell Publishing Ltd. ((United Kingdom) Oxford)
issn 1368-4221
Publication status Publié
Affiliations UCL - SSH/LIDAM/CORE - Center for operations research and econometrics
UCL - EUEN/CORE - Center for operations research and econometrics
Keywords Bayesian inference ; GARCH ; Markov-switching
Links
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Bibliographic reference Bauwens, Luc ; Preminger, Arie ; Rombouts, Jeroen. Theory and inference for a Markov switching GARCH model. In: The Econometrics Journal, Vol. 13, no. 2, p. 218-244 (2010)
Permanent URL http://hdl.handle.net/2078.1/73492